It has been widely speculated that insect populations are currently experiencing declines, and numerous recent studies have turned to historical collections and the literature as a means of quantifying population trajectories. Yet, populations are dynamic, and thus effective examination requires long-term observation, but how much? How do we know when enough data has been collected to establish the trajectory of a population?
The complexity of understanding insect population trajectory can be illustrated with a simple, and familiar, example. Fireflies are a culturally beloved and functionally significant insect predator whose populations are speculated to be in decline. If true, declines could have detrimental effects not only for the ecological communities the fireflies exist in, but culturally for the humans who enjoy them as well. Yet, insect populations are r-selected, meaning natural variability due to boom-bust dynamics may mask net trends. To address these issues, we built a non-random resampling algorithm which uses existing observational data and a moving window approach to re-sample and re-analyze at all possible sampling durations. We then compile results from each analysis iteration to gain understanding on the duration of sampling required to detect consistent patterns and the frequency of short-term, misleading trends.
Results/Conclusions
Using data generated by a long-term insect trapping study at Michigan State University’s Kellogg Biological Station LTER, we compiled 12 years of firefly (primarily Photinus pyralis) activity data. Applying the moving-window algorithm, we found that firefly populations were net stable over the 12-year period. However, short-term patterns exhibiting both declines and increases were common, statistically significant, and associated with high values of fit statistics like R2, likely relating to an apparent (~6 year) cyclical dynamic this taxon exhibits at the site. Indeed, we observed that in studies conducted for durations less than the approximate 6-7 year stabilization across habitat types, misleading trajectories would be observed in approximately two-thirds of cases. This study highlights the innate complexity in forecasting ecological trajectories from short-term observations and furthermore, the need for long-term studies to capture these complexities.
Although we are undoubtedly in a time of unprecedented biodiversity loss, we do not find evidence that the fireflies of southwestern MI have changed in their dynamic appreciably in recent years. Understanding the natural variability and temporal processes associated with a population is key to fostering effective species conservation plans, as more superficial examinations will neither capture the correct trajectory nor reliably isolate drivers of dynamics.